Isaca AAISM Online Practice Questions and Exam Preparation

Isaca AAISM Online Questions & Answers

• Question 111:

  After deployment, an AI model's output begins to drift outside of the expected range. Which of the following is the development team's BEST course of action?

  A. Take the AI model offline
  B. Adjust the hyperparameters of the AI model
  C. Create an emergency change request to correct the issue
  D. Return to an earlier phase in the AI life cycle
  D. Return to an earlier phase in the AI life cycle

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  AAISM emphasizes that when model drift occurs, the best response is not a quick fix but rather to revisit an earlier phase of the AI life cycle to address data quality, retraining, or evaluation processes. Simply taking the model offline halts functionality without resolution, while adjusting hyperparameters or issuing emergency changes treats the symptom rather than the root cause. Proper governance requires returning to the design or training phases to re-establish stability, accuracy, and compliance of the

  model. Thus, the correct approach is to return to an earlier AI lifecycle phase.

  References:

  AAISM Exam Content Outline - AI Risk Management (Model Drift and Lifecycle Responses)

  AI Security Management Study Guide - Continuous Improvement in AI Lifecycle

• Question 112:

  A post-incident investigation finds that an AI-powered anti-money laundering system inadvertently allowed suspicious transactions because certain risk signals were disabled to reduce false positives. Which of the following governance failures does this BEST demonstrate?

  A. Lack of sufficient computing resources for the AI system
  B. Insufficient model validation and change control processes
  C. Excessive reliance on external consultants for model design
  D. Absence of metrics and dashboard for analysts
  B. Insufficient model validation and change control processes

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  AAISM requires formal model change governance: documented justification, risk assessment, validation /verification (VandV), approvals, and post-deployment monitoring when altering features, thresholds, or signals. Disabling risk indicators to reduce false positives without rigorous validation and controlled rollout reflects a failure in model validation and change control, which AAISM treats as a core safeguard against unintended harms and regulatory breaches.

  References: AI Security Management. (AAISM) Body of Knowledge - Model Risk Governance; Change Management and Approvals; Validation/Verification Requirements. AAISM Study Guide - Control Gates for Feature/Threshold Changes; Post-Change Monitoring and Backout Criteria.

• Question 113:

  Which of the following BEST describes how supervised learning models help reduce false positives in cybersecurity threat detection?

  A. They analyze patterns in data to group legitimate activity from actual threats
  B. They use real-time feature engineering to automatically adjust decision boundaries
  C. They learn from historical labeled data
  D. They dynamically generate new labeled data sets
  C. They learn from historical labeled data

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  According to AAISM technical content, supervised learning models reduce false positives by learning from historical labeled data that distinguishes between legitimate activity and actual threats. This training enables the model to recognize patterns and improve its discrimination ability over time. Grouping patterns (A) describes clustering, an unsupervised method. Real-time feature engineering (B) and generating new labeled data (D) are advanced techniques but not the fundamental supervised learning

  approach. The essence of supervised learning is leveraging labeled data to minimize misclassification, including false positives.

  References:

  AAISM Exam Content Outline - AI Technologies and Controls (Machine Learning Approaches) AI Security Management Study Guide - Supervised Learning for Threat Detection

• Question 114:

  Which attack type is MOST likely to cause model drift?

  A. Model stealing
  B. Perfect knowledge
  C. Data poisoning
  D. Membership inference
  C. Data poisoning

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  AAISM defines data poisoning as directly capable of causing model drift because corrupted training data shifts the statistical distribution, leading to degraded or unsafe performance.

  Model stealing (A) extracts model behavior but does not cause drift. Perfect knowledge (B) is an attacker capability, not an attack causing drift. Membership inference (D) attacks privacy, not performance.

  References: AAISM Study Guide - Model Drift Causes; Data Poisoning Impact.

• Question 115:

  Which of the following types of data is used to tune hyperparameters?

  A. Validation
  B. Configuration
  C. Training
  D. Test
  A. Validation

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  According to AAISM, hyperparameter tuning uses validation data, not training or test data. Validation datasets are specifically designed to evaluate different hyperparameter configurations without contaminating the training or testing sets. Training data (C) teaches the model. Test data (D) is used only for final evaluation. Configuration data (B) is unrelated to performance optimization.

  References: AAISM Study Guide - AI Model Evaluation; Role of Validation Data in Hyperparameter Tuning.

• Question 116:

  Which of the following strategies is the MOST effective way to protect against AI data poisoning?

  A. Increasing model complexity to better handle data variations
  B. Ensuring the model is trained on diverse data sources
  C. Incorporating more features and data into model training
  D. Using robust data validation techniques and anomaly detection
  D. Using robust data validation techniques and anomaly detection

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  AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.

  References: AI Security Management. (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion and Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.

• Question 117:

  Which of the following should be the PRIMARY objective of implementing differential privacy techniques in AI models leveraging fraud detection systems?

  A. Enhancing the accuracy of predictions to desired levels
  B. Increasing model training speed for an efficient launch
  C. Protecting individual data contributions while allowing statistical analysis
  D. Reducing computational resources required for the model training phase
  C. Protecting individual data contributions while allowing statistical analysis

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  Differential privacy aims to protect the privacy of any single individual's data contribution while still enabling useful aggregate learning and statistical analysis. Noise mechanisms are calibrated so that results remain informative for modeling (e.g., fraud patterns) without revealing whether any particular person's data was included or enabling inference about them. Accuracy, speed, and compute efficiency can be secondary considerations, but the primary objective is privacy protection with utility preserved.

  References: AI Security Management (AAISM) Body of Knowledge: Privacy-Preserving ML; Differential Privacy Objectives and Mechanisms. AAISM Study Guide: Individual Contribution Protection; Utility-Privacy Trade-offs and Calibration in Applied Models.

• Question 118:

  Which of the following key risk indicators (KRIs) is MOST relevant when evaluating the effectiveness of an organization's AI risk management program?

  A. Number of AI models deployed into production
  B. Percentage of critical business systems with AI components
  C. Percentage of AI projects in compliance
  D. Number of AI-related training requests submitted
  C. Percentage of AI projects in compliance

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  AAISM identifies percentage of AI projects in compliance as the most relevant KRI for evaluating AI risk management effectiveness. This metric directly reflects adherence to governance, regulatory, and security requirements. The number of models deployed (A) or systems with AI components (B) indicate scale, not risk management quality. Training requests (D) show awareness levels but do not measure effectiveness of risk management. Compliance percentage provides a direct, measurable indication of

  how well risks are being governed and mitigated.

  References:

  AAISM Exam Content Outline - AI Risk Management (Risk Metrics and Compliance)

  AI Security Management Study Guide - Key Risk Indicators in AI Programs

• Question 119:

  Which approach should an organization prioritize to effectively verify the security of its AI models?

  A. Automating vulnerability identification
  B. Developing a testing strategy including AI-specific threat modeling and adversarial attack simulations
  C. Testing team competencies in IT threat mitigation
  D. Using standard penetration testing methods
  B. Developing a testing strategy including AI-specific threat modeling and adversarial attack simulations

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  The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems. Effective AI security testing requires: AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)

  Adversarial attack simulations (white-box, black-box, gradient-based attacks)

  Evaluation of robustness and manipulation resistance

  Option B captures these requirements precisely.

  Options A, C, and D do not address AI-specific attack vectors.

  References: AAISM Study Guide - AI Security Testing and Adversarial Evaluation.

• Question 120:

  Which of the following would BEST ensure a proper business continuity plan (BCP) is in place for an AI solution?

  A. Enhancing monitoring and detection of model failures and anomalies
  B. Implementing access controls to protect the AI system from unauthorized use
  C. Testing the AI infrastructure failover mechanisms
  D. Increasing the detail of AI solution backup and restoration processes
  C. Testing the AI infrastructure failover mechanisms

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  Effective AI BCP requires validation through exercises and controlled failover tests to prove recovery objectives can be met in practice. Merely documenting backups (Option D), hardening access (Option B), or improving monitoring (Option A) does not confirm that the AI stack-data pipelines, feature stores, model registries, inference services, and dependent infrastructure-can actually fail over and recover within RTO / RPO. AAISM prescribes periodic BCP/DR testing (including model artifact restoration, configuration reconstitution, dependency failover, and data pipeline continuity) to verify readiness and identify gaps before real incidents. References:AI Security Management. (AAISM) Body of Knowledge: Business Continuity and Disaster Recovery for AI; Validation and Exercising of Continuity Plans; RTO/RPO for Models, Data, and Pipelines. AAISM Study Guide: Operational Resilience for AI Systems; BCP/DR Test Scenarios (model registry, feature store, pipeline recovery); Continuity Metrics and Evidence of Readiness.